HOMELAND SECURITY WILL LET COMPUTERS PREDICT WHO MIGHT BE A TERRORIST ON YOUR PLANE — JUST DON’T ASK HOW IT WORKS

YOU’RE RARELY ALLOWED to know exactly what’s keeping you safe. When you fly, you’re subject to secret rules, secret watchlists, hidden cameras, and other trappings of a plump, thriving surveillance culture. The Department of Homeland Security is now complicating the picture further by paying a private Virginia firm to build a software algorithm with the power to flag you as someone who might try to blow up the plane.

The new DHS program will give foreign airports around the world free software that teaches itself who the bad guys are, continuing society’s relentless swapping of human judgment for machine learning. DataRobot, a northern Virginia-based automated machine learning firm, won a contract from the department to develop “predictive models to enhance identification of high risk passengers” in software that should “make real-time prediction[s] with a reasonable response time” of less than one second, according to a technical overview that was written for potential contractors and reviewed by The Intercept. The contract assumes the software will produce false positives and requires that the terrorist-predicting algorithm’s accuracy should increase when confronted with such mistakes. DataRobot is currently testing the software, according to a DHS news release.

The contract also stipulates that the software’s predictions must be able to function “solely” using data gleaned from ticket records and demographics — criteria like origin airport, name, birthday, gender, and citizenship. The software can also draw from slightly more complex inputs, like the name of the associated travel agent, seat number, credit card information, and broader travel itinerary. The overview document describes a situation in which the software could “predict if a passenger or a group of passengers is intended to join the terrorist groups overseas, by looking at age, domestic address, destination and/or transit airports, route information (one-way or round trip), duration of the stay, and luggage information, etc., and comparing with known instances.”

DataRobot’s bread and butter is turning vast troves of raw data, which all modern businesses accumulate, into predictions of future action, which all modern companies desire. Its clients include Monsanto and the CIA’s venture capital arm, In-Q-Tel. But not all of DataRobot’s clients are looking to pad their revenues; DHS plans to integrate the code into an existing DHS offering called the Global Travel Assessment System, or GTAS, a toolchain that has been released as open source software and which is designed to make it easy for other countries to quickly implement no-fly lists like those used by the U.S.

According to the technical overview, DHS’s predictive software contract would “complement the GTAS rule engine and watch list matching features with predictive models to enhance identification of high risk passengers.” In other words, the government has decided that it’s time for the world to move beyond simply putting names on a list of bad people and then checking passengers against that list. After all, an advanced computer program can identify risky fliers faster than humans could ever dream of and can also operate around the clock, requiring nothing more than electricity. The extent to which GTAS is monitored by humans is unclear. The overview document implies a degree of autonomy, listing as a requirement that the software should “automatically augment Watch List data with confirmed ‘positive’ high risk passengers.”

The document does make repeated references to “targeting analysts” reviewing what the system spits out, but the underlying data-crunching appears to be almost entirely the purview of software, and it’s unknown what ability said analysts would have to check or challenge these predictions. In an email to The Intercept, Daniel Kahn Gillmor, a senior technologist with the American Civil Liberties Union, expressed concern with this lack of human touch: “Aside from the software developers and system administrators themselves (which no one yet knows how to automate away), the things that GTAS aims to do look like they could be run mostly ‘on autopilot’ if the purchasers/deployers choose to operate it in that manner.” But Gillmor cautioned that even including a human in the loop could be a red herring when it comes to accountability: “Even if such a high-quality human oversight scheme were in place by design in the GTAS software and contributed modules (I see no indication that it is), it’s free software, so such a constraint could be removed. Countries where labor is expensive (or controversial, or potentially corrupt, etc) might be tempted to simply edit out any requirement for human intervention before deployment.”

“Countries where labor is expensive might be tempted to simply edit out any requirement for human intervention.”

For the surveillance-averse, consider the following: Would you rather a group of government administrators, who meet in secret and are exempt from disclosure, decide who is unfit to fly? Or would it be better for a computer, accountable only to its own code, to make that call? It’s hard to feel comfortable with the very concept of profiling, a practice that so easily collapses into prejudice rather than vigilance. But at least with uniformed government employees doing the eyeballing, we know who to blame when, say, a woman in a headscarf is needlessly hassled, or a man with dark skin is pulled aside for an extra pat-down.

If you ask DHS, this is a categorical win-win for all parties involved. Foreign governments are able to enjoy a higher standard of security screening; the United States gains some measure of confidence about the millions of foreigners who enter the country each year; and passengers can drink their complimentary beverage knowing that the person next to them wasn’t flagged as a terrorist by DataRobot’s algorithm. But watchlists, among the most notorious features of post-9/11 national security mania, are of questionable efficacy and dubious legality. A 2014 report by The Intercept pegged the U.S. Terrorist Screening Database, an FBI data set from which the no-fly list is excerpted, at roughly 680,000 entries, including some 280,000 individuals with “no recognized terrorist group affiliation.” That same year, a U.S. district court judge ruled in favor of an ACLU lawsuit, declaring the no-fly list unconstitutional. The list could only be used again if the government improved the mechanism through which people could challenge their inclusion on it — a process that, at the very least, involved human government employees, convening and deliberating in secret.

Diagram from a Department of Homeland Security technical document illustrating how GTAS might visualize a potential terrorist onboard during the screening process.

Document: DHS

But what if you’re one of the inevitable false positives? Machine learning and behavioral prediction is already widespread; The Intercept reportedearlier this year that Facebook is selling advertisers on its ability to forecast and pre-empt your actions. The consequences of botching consumer surveillance are generally pretty low: If a marketing algorithm mistakenly predicts your interest in fly fishing where there is none, the false positive is an annoying waste of time. The stakes at the airport are orders of magnitude higher.

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